1. Field of the Invention
The invention relates to a semiconductor and optical scattering measurement field, and more particularly, to a method for extracting a critical dimension of a semiconductor nanostructure.
2. Description of the Related Art
For the purpose of implementing operability, repeatability, and extendibility of nano manufacturing process, and ensuring reliability, uniformity, and economical and large-scale production based on the nano technology, it is very important to perform fast, non-destructive, and low-cost measurement on 3D morphology parameters of structures, including feature linewidth (critical dimensions), height, cycles, sidewall angles and so on, during the nano manufacturing process.
In the semiconductor and optical measurement field, an optical scatterometer is the most popular-used device for measuring critical dimensions. Measurement of the optical scatterometer includes forward optical modeling and reverse seeking. Forward optical modeling is to perform optical scattering field simulation on geometric models of nano structures to be measured, whereby extracting simulation spectra. Reverse seeking includes continuously comparing the measured spectra with the simulation spectra, and parameters of a model corresponding to a simulation spectrum with the highest similarity are parameters of the nano structures to be measured. The most popular-used method during reverse seeking of the optical scatterometer is a library-matching-based method. The method includes: first constituting a simulation spectra database for a structure model to be measured, each independent spectrum in the database corresponding to a model determined by a parameter value, and searching for a simulation spectrum most similar to each measured spectrum in the database according to an evaluation function, a parameter value of a model corresponding to the simulation spectrum being that of the structure to be measured. However, the spectra database often includes large amount of simulation spectra, and the number of the spectra is to grow in a geometrical progression with increase of parameters of the nano structures, expansion of simulation parameters, and rise in requirement for accuracy of parameters to be extracted. To meet requirements for real-time feature and rapidity in industries, and to implement fast mapping of measured spectra in a large-scale spectra database, a new mapping method needs to be launched, and an old full-library searching method needs to be discarded.
Library-mapping-based extraction of geometric parameters of a nano structure includes establishment of a simulation database, and searching in a database. Searching in a database includes searching for a simulation spectrum most similar to a measured spectrum in the database according to certain rules, and is a typical problem of maximum proximity searching. Traditional methods for solving the maximum proximity searching problem include a direct whole-library searching method, a k-d tree method, a clustering analysis method, a local sensitivity hashing and so on. However, no good global optimal result can be obtained when the above-mentioned methods, except for the whole-library searching method, are used to solve problems such as most-similar spectra searching. This is because spectra often have non-obvious characteristics, and these methods make use of one or more characteristics of parameters to be searched. A GPU (Graphic Processing Unit) is also employed to measure mapping of most-similar simulation spectra in a database. The GPU is a hardware acceleration module especially designed for processing images and featuring higher data processing speed, better data processing, and concurrent computation capability with respect to a CPU. Fast mapping of spectra can be achieved by arranging multiple simulation spectra into a matrix denoting spectra images, and thus fast extraction of geometric parameters. However, problems with this method are that, with further expansion of the simulation spectra database, more powerful and high-efficient GPUs must be used, which limits scalability of this category of hardware-acceleration-based searching method.